Human Capital Measures and Its Effect on Economic Convergence in China Haizheng Li School of Economics Georgia Institute of Technology & China Center for Human Capital and Labor Market Research Haizheng.li@econ.gatech.edu

Human capital and reginal income inequality Research shows that human capital has a significant effect on reginal income inequality in China human capital measures should in general reflect the income disparity across regions

Traditional education-based human capital measures Traditional human capital measures are mostly education-based. They can only partially capture the amount of human capital Illiteracy rates Enrolment rates AEDU: average education of the labor force PLHS : the proportion of labor force with high school education or above PLC : the proportion of labor force with college

J-F based human capital measure Jorgenson-Fraumeni (J-F) lifetime income approach proxies an individual’s human capital based on earnings. J-F approach includes various aspects of human capital accumulation-a comprehensive measure education, on-the-job training, other unobserved aspects such as health and abilities as well as unobserved school quality. The J-F framework maintains the neoclassical assumption wages represent marginal products of labor uses wage returns to capture the productivity gains from human capital investments.

Advantages of the J-F based human capital measure Incorporates age information and thus can capture the effect of age structure (such as population aging) on the stock of human capital. Can estimate human capital for young people who are not in the labor market yet. Other measures used information on earnings can only estimate human capital for labor force. The J-F method can estimate human capital stock in production use (labor force) and human capital reserve (young people). Estimates human capital as a monetary value and can be easily interpreted, for example, it can be compared with physical capital stock, and is directly useful for policy analysis.

Research Team and Sponsors Human capital project: starting in 2008 Research Team China Center for Human Capital and Labor Market Research (CHLR) at the CUFE All special-term faculty, full-time faculty, doctoral and master students, and staff Sponsors National Natural Science Foundation of China, supported 3 waves till 2021. Central University of Finance and Economics (CUFE), via special grant every year 中国人力资本与劳动经济研究中心 China Center for Human Capital and Labor Market Research

China Center for Human Capital and Labor Market Research Research Outcomes Provincial human capital panel data 31 provinces/cities, 1985-2015 Various human capital stock measures, such as Total human capital(HC), Labor force human capital(LFHC), Per capita HC, Average LFHC, and more… Traditional human capital measure, such as average years of education, average age… Provincial physical capital estimates Panel data compatible with human capital data Provincial cross-province living-cost adjustment index For PPP comparison Description of data 中国人力资本与劳动经济研究中心 China Center for Human Capital and Labor Market Research

China Human Capital Report Series, 2009-2017 2010 2011 2012 2013 2014 2015 2016 2017 Description of data 中国人力资本与劳动经济研究中心 China Center for Human Capital and Labor Market Research

China Center for Human Capital and Labor Market Research Research Outcomes A large database, including raw data, processed data, and results, is freely available to public downloading at, http://humancapital.cufe.edu.cn/ Increasing number of downloading and requests Annual international conference on human capital 2009-2017 The acceptance rate for presentation has been 50-60% The 10th anniversary in December this year Description of data 中国人力资本与劳动经济研究中心 China Center for Human Capital and Labor Market Research

J-F based human capital measure The J-F approach estimates each individual’s expected lifetime income and then aggregates all individuals together to get total human capital stock The estimation is conducted in a backward recursive fashion beginning with the retirement age. life cycle is divided into five stages retirement, work-only work-school school-only pre-school 0% 40% 60%

Coefficient of Variation (CV) across provinces (2014) Disparity of human capital cross provinces ---based on the traditional education measures and the J-F measure Coefficient of Variation (CV) across provinces (2014) GDP per worker: 44% AEDU : 8% PLHS : 27% PLC: 44% but covers only a small portion of labor force, less than 16% PCLF(per capita labor force human capital ) : CV is 55% The J-F human capital measures more closely reflect these regional inequalities as represented by GDP per worker

disparity of human capital cross provinces ---based on the traditional education measures and the J-F measure

regional inequality in 2014: ratio of the east region to the west for disparity of human capital cross provinces ---based on the traditional education measures and the J-F measure regional inequality in 2014: ratio of the east region to the west for GDP per worker :1.94 AEDU : 1.09 PLHS :1.33 PLC: 1.33 However, the ratio for the PCLF is1.88, closest to that of GDP per worker

The trend of regional GDP per worker and different human capital measures.

Relation between education-based human capital measure and J-F human capital In 2014, at national level education is a major component of the human capital US (2013) 89% (age 25+): 2014 Hongkong 75%, Taiwai 86%

Relation between education-based human capital measure and J-F human capital

Divisia Decomposition of Education Effect on J-F Human Capital The contribution of education comes from growth of the population with a specific level of education and the share of human capital of this education group In particular, the annual contribution of education (e) to per capita human capital (quality) growth can be written as

The Education Effect on J-F Human Capital Among regions, education contributed the most to the labor force human capital (PCLF). The expansion of education in China mostly benefited the labor force, because it occurred mostly at the high school and college level In particular, from 1985 to 2014, the average annual growth proportion of college graduates in the labor force :10.4% proportion of high school or above : 4.0%

disparity of human capital cross provinces ---based on the traditional education measures and the J-F measure Factor Contributions to Labor Force Human Capital Quality (PCLF) Growth 1985-1994 Factor Contributions to Labor Force Human Capital Quality (PCLF) Growth 1995-2014

Human Capital and the Economic Convergence Mechanism As a benchmark, we start with the Mankiw, Romer and Weil (1992) (henceforth MRW).

New development in measuring human capital Generalized approach to human capital accounting (Jones, AER, 2014) Advantages relative to J-F method. Do not need a perfect substitution assumption for different categories of labor. Develop a human capital aggregator, Generalized Division of Labor (GDL) with function form being flexible for elasticity of subsitution among different categories of labor. Description of data 中国人力资本与劳动经济研究中心 China Center for Human Capital and Labor Market Research

Generalized Division of Labor (GDL) aggregator Aggregation of human capital Aggregated human capital with constant return to scale: where is human capital of skill type, and being labor quality and quantity Generalized Division of Labor (GDL) aggregator: where θ is elasticity of substitution Description of data Generalized human capital aggregation : where . 中国人力资本与劳动经济研究中心 China Center for Human Capital and Labor Market Research

Pros and Cons of Various Human Capital Measures In the neoclassical approach based on unskilled worker equivalents, the human capital stock is calculated using the current relative wage as weights when summing up all labor together. In this case, for example, if a 23 years old college graduate has the same wage as a 58 years old high school graduate, they are considered to have the same amount of the “unskilled worker equivalent” units. For education-based measure, a labor force with all old workers has very different amount of human capital compared to a labor force of the same size but with all young workers, But their education measures can be identical. The J-F framework can better capture the joint effect of age and education on the amount of human capital. but assume perfect substitution

Challenges on Human Capital Measurement Different measures of human capital are not perfect substitutes, each captures a different aspect of human capital Different measures show varying effects on economic growth and income inequality All measures rely on various restrictive assumptions Different measures will have different policy implications, e.g., formal schooling, on-the-job learning, school quality, health, … Which measure to choose? Criteria for it? Can better measures be developed? Challenges ahead……

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